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- W2061857234 abstract "MapReduce is a scalable parallel computing framework for big data processing. It exhibits multiple processing phases, and thus an efficient job scheduling mechanism is crucial for ensuring efficient resource utilization. There are a variety of scheduling challenges within the MapReduce architecture, and this paper studies the challenges that result from the overlapping of the “map” and “shuffle” phases. We propose a new, general model for this scheduling problem, and validate this model using cluster experiments. Further, we prove that scheduling to minimize average response time in this model is strongly NP-hard in the offline case and that no online algorithm can be constant-competitive. However, we provide two online algorithms that match the performance of the offline optimal when given a slightly faster service rate (i.e., in the resource augmentation framework). Finally, we validate the algorithms using a workload trace from a Google cluster and show that the algorithms are near optimal in practical settings." @default.
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- W2061857234 date "2013-10-01" @default.
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- W2061857234 title "Joint optimization of overlapping phases in MapReduce" @default.
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- W2061857234 doi "https://doi.org/10.1016/j.peva.2013.08.013" @default.
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